Financial Services
Partnering with global financial services companies to improve portfolio management, customer engagement, and business efficiencies
Using Relational Knowledge Graphs
to solve key banking challenges
Knowledge Extraction
Challenge: Knowledge Extraction
Unstructured data, including text or documents, represents the majority of data in enterprises. For financial institutions, contractual documents contain a wealth of knowledge that is difficult and expensive to tap into, but that the business relies on heavily.
Solution: Democratizing Data
RelationalAI technology leverages AI/ML algorithms to extract key information from text with limited to no input from users. The extracted information, together with domain knowledge, is then structured in a relational knowledge graph.
The newly constructed knowledge graph enables self-service for the business through querying, search, and other use cases. This allows the business to work more independently to identify contractual risks or loopholes, and prevent business losses caused by non-compliance.
Impact
20-25% increase in efficiency gains driven by better search and query answering.
Portfolio Management
Challenge: Portfolio Management
Portfolio Managers spend a large amount of their time manually rebalancing and reviewing their clients' portfolios. The rebalancing is usually managed in Excel, making the process non-scalable, slow, and suboptimal.
Solution: Automated Portfolio Management
RelationalAI's rebalancing tool, SmartRebalancer, blends machine learning, reasoning, and optimization to unlock the next generation of portfolio management. SmartRebalancer automates trade generation and tax management, giving a holistic view of the portfolio and enabling new levels of customer customizations, resulting in higher returns.
Impact
Increase in after-tax portfolio return by ~1%.
Commercial Excellence
Challenge: Commercial Excellence
Customers want a unified, personalized experience across channels. However, the customer journey today is fragmented across multiple departments, and the associated data reflects these silos. This makes cross-selling, attribution, and personalization difficult.
Solution: Interconnected Data
Personalization helps both the seller's ability to cross-sell and the online customer experience. Building on top of a relational knowledge graph allows more data types to be connected and domain knowledge to be embedded. The result is increased revenue and customer satisfaction.
Impact
10-20% revenue uplift, 5-10% increase in testing speed, and 20-30% increase in customer engagement and satisfaction.